title: “Beatles Commands” output: html_notebook editor_options: chunk_output_type: inline
dtm ## <<DocumentTermMatrix (documents: 187, terms: 1719)>> ## Non-/sparse entries: 7000/314453 ## Sparsity : 98% ## Maximal term length: 17 ## Weighting : term frequency (tf)
dtm1 <-removeSparseTerms(dtm,0.99)
dtm1 ##<<DocumentTermMatrix (documents: 187, terms: 776)>> ## Non-/sparse entries: 6057/139055 ## Sparsity : 96% ## Maximal term length: 16 ## Weighting : term frequency (tf)
dtm2 <-removeSparseTerms(dtm,0.98)
dtm2 ##<<DocumentTermMatrix (documents: 187, terms: 412)>> ## Non-/sparse entries: 5225/71819 ## Sparsity : 93% ## Maximal term length: 10 ## Weighting : term frequency (tf)
dtm3 <-removeSparseTerms(dtm,0.97)
dtm3 ##<<DocumentTermMatrix (documents: 187, terms: 274)>> ## Non-/sparse entries: 4621/46617 ## Sparsity : 91% ## Maximal term length: 10 ## Weighting : term frequency (tf)
dtm4 <-removeSparseTerms(dtm,0.96)
dtm4 ##<<DocumentTermMatrix (documents: 187, terms: 219)>> ## Non-/sparse entries: 4267/36686 ## Sparsity : 90% ## Maximal term length: 10 ## Weighting :term frequency (tf)
dtmbeatles <- removeSparseTerms(dtm,0.9)
setwd("/Users/aleynakay/Downloads/PSYC 5710/Beatles_Assignment")
lyrics_beatles <- read.csv("~/Downloads/PSYC 5710/Beatles_Assignment/lyrics_beatles.csv")
View(lyrics_beatles)
#install.packages("tm")
#install.packages("SnowballC")
library(tm)## Loading required package: NLPlibrary(SnowballC)
beatles <- read.csv("lyrics_beatles.csv", header = TRUE, stringsAsFactors = FALSE)
beatles[17,4]## [1] 1963corpus <- Corpus(VectorSource(beatles[,3]))
corpus## <<SimpleCorpus>>
## Metadata: corpus specific: 1, document level (indexed): 0
## Content: documents: 187corpus <- tm_map (corpus, tolower)## Warning in tm_map.SimpleCorpus(corpus, tolower): transformation drops documentscorpus <- tm_map(corpus, removePunctuation)## Warning in tm_map.SimpleCorpus(corpus, removePunctuation): transformation drops
## documentscorpus <- tm_map(corpus, removeNumbers)## Warning in tm_map.SimpleCorpus(corpus, removeNumbers): transformation drops
## documentscorpus <- tm_map(corpus, removeWords, stopwords("english"))## Warning in tm_map.SimpleCorpus(corpus, removeWords, stopwords("english")):
## transformation drops documentscorpus <- tm_map(corpus, stemDocument)## Warning in tm_map.SimpleCorpus(corpus, stemDocument): transformation drops
## documentsdtm <- DocumentTermMatrix(corpus)
dtmbeatles <- removeSparseTerms(dtm,0.9)
dtmbeatles## <<DocumentTermMatrix (documents: 187, terms: 72)>>
## Non-/sparse entries: 2515/10949
## Sparsity : 81%
## Maximal term length: 7
## Weighting : term frequency (tf)dtmbeatles ## <<DocumentTermMatrix (documents: 187, terms: 72)>> ## Non-/sparse entries: 2515/10949 ## Sparsity : 81% ## Maximal term length: 7 ## Weighting : term frequency (tf)
dfbeatles <-data.frame(as.matrix(dtmbeatles),stringsAsFactors = FALSE)
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## back can cant cri ive one thing will world come girl leav let like say tell
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## 187 0 0 0 0 0 0 0 2 1 0 0 0freq.dtm <- sort(colSums(dfbeatles),decreasing=TRUE)
head(freq.dtm) ## love know dont yeah come need
## 589 301 249 226 189 181#install.packages("ggplot2")
library(ggplot2) ##
## Attaching package: 'ggplot2'## The following object is masked from 'package:NLP':
##
## annotatefreq.data <- data.frame(word=names(freq.dtm),freq=freq.dtm)
head(freq.data)## word freq
## love love 589
## know know 301
## dont dont 249
## yeah yeah 226
## come come 189
## need need 181freq.plot <- ggplot(freq.data, aes(reorder(word, freq), freq)) + geom_col() + xlab(NULL) + coord_flip() + ylab("Frequency") + theme(text = element_text(size = 15), axis.text.y = element_text(size=15, angle = 45))
freq.plot
#install.packages("qgraph")
#install.packages("plotly")
#install.packages("dplyr")
library(qgraph)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unioncor.terms <- cor_auto(dfbeatles)## Variables detected as ordinal: heart; ooh; way; alway; thing; world; leav; want; around; away; take; blue; show; said; there; tri; word; hear; though; yes; head; mind; eye; that; right; sing; wait; youv; everyth; find; turn# Variables detected as ordinal: heart; ooh; way; alway; thing; world; leav; want; around; away; take; blue; show; said; there; tri; word; hear; though; yes; head; mind; eye; that; right; sing; wait; youv; everyth; find; turn
a <- list(showticklabels=TRUE,tickangle= -45)
plot.cor <- plot_ly(x = colnames(cor.terms),y = colnames(cor.terms), z = cor.terms, type = "heatmap") %>% layout (xaxis=a, showlegend = FALSE, margin = list (l=100, b=100, r=100, u=100))
plot.corhtmlwidgets::saveWidget(as_widget(plot.cor),file="beatles.cor.html")